SENTIMENT ANALYSIS RELATED TO DIGITAL BANK SERVICES IN INDONESIA USING NAïVE BAYES AND MAXIMUM ENTROPY METHODS
Physics is a part of science that essentially is a collection of knowledge, ways of thinking, methods for investigating the nature of the universe, as well as interactions with technology and society. Interactions between technology and society can also be referred to as complex systems, which ha...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/75212 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Physics is a part of science that essentially is a collection of knowledge, ways of
thinking, methods for investigating the nature of the universe, as well as interactions
with technology and society. Interactions between technology and society can also
be referred to as complex systems, which have been applied to machine learning
and artificial intelligence. Technological advancements that integrate all aspects
have brought about significant changes, including in the banking industry. The
consumption patterns of society shifting towards digital have prompted banks to
undergo transformation by developing digital banking services in Indonesia.
Technological progress has also made it easier for people to obtain information and
express opinions through social media platforms like Twitter. This research aims to
analyze the sentiment regarding digital banking services in Indonesia using the
Naïve Bayes and Maximum Entropy methods. The analysis is conducted in several
steps, starting with data crawling on Twitter using RapidMiner with keywords such
as "bank digital," "bank jago," "neobank," "bank jenius," "seabank," and "blu bca."
This is followed by data preprocessing and sentiment classification into positive or
negative using the Naïve Bayes method in RapidMiner and the Maximum Entropy
method using Python. Based on the data processing results, an accuracy of 82.98%
was obtained in the first stage, and 58.89% in the second stage using the Naïve
Bayes method, while with the Maximum Entropy, an accuracy of 85.11% was
achieved in the first stage and 90.44% in the second stage. Furthermore, variations
in the number of training data were explored, and it was found that increasing the
number of training data does not always lead to an improvement in accuracy. The
conclusion drawn from this research is that the Maximum Entropy method yields
higher accuracy and precision compared to the Naïve Bayes method in this study. |
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